Identiﬁcation of quantitative trait loci inﬂuencing foliar ... · Martin L. Henery, Gavin F. Moran, Ian R. Wallis and William J. Foley School of Botany and Zoology, Australian

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1

Research

Blackwell Publishing Ltd

Identification of quantitative trait loci influencing foliar concentrations of terpenes and formylated phloroglucinol

compounds in

Eucalyptus nitens

Martin L. Henery, Gavin F. Moran, Ian R. Wallis and William J. Foley

School of Botany and Zoology, Australian National University, Canberra ACT 0200, Australia

Summary

•

Leaves of eucalypt species contain a variety of plant secondary metabolites,including terpenoids and formylated phloroglucinol compounds (FPCs). Bothterpene and FPC concentrations are quantitative traits that can show large variationwithin a population and have been shown to be heritable. The molecular geneticbasis of this variation is currently unknown.

•

Progeny from a field trial of a three-generation mapping pedigree of

Eucalyptusnitens

were assayed for terpenes and FPCs. Quantitative trait loci (QTL) analyseswere conducted using a map constructed from 296 markers to locate regions of thegenome influencing foliar concentrations of these plant secondary compounds.

•

A large number of significant QTL for 14 traits were located across nine linkagegroups, with significant clustering of QTL on linkage groups 7, 8 and 9. As expected,QTL for biosynthetically related compounds commonly colocated, but QTL for unre-lated monterpenes and FPCs also mapped closely together.

•

Colocation of these QTL with mapped candidate genes from the various biosyntheticpathways, and subsequent use of these genes in association mapping, will assistin determining the causes of variation in plant secondary metabolites in eucalypts.

Key words:

Eucalyptus

, formylated phloroglucinol compounds (FPCs), geneticmapping, monoterpenes, quantitative trait loci (QTL), sesquiterpenes.

New Phytologist

(2007)

doi

: 10.1111/j.1469-8137.2007.02159.x

© The Authors (2007). Journal compilation ©

New Phytologist

(2007)

Author for correspondence:

Martin L. HeneryTel:

+

61 2612525Fax:

+

61 261255573Email: [email protected]

Received:

8 March 2007

Accepted:

18 May 2007

Introduction

Trees in the genus

Eucalyptus

contain complex mixtures ofplant secondary metabolites, including terpenoids, hydroly-sable and condensed tannins, flavonoids, long-chain ketones(Brophy & Southwell, 2002), cyanogenic glycosides (Gleadow

et al

., 1998) and formylated phloroglucinol compounds(FPCs) (Moore

et al

., 2004a). Many of these compounds areputative defensive chemicals, but evidence for their role inresistance to insect herbivores is generally lacking. Like othertaxa in the Myrtaceae, terpenes accumulate in foliar oil glands,and in some eucalypts these can account for large percentagesof leaf mass (

>

7% FW) (Boland

et al

., 1991). Despite thesehigh levels of foliar terpenes, many experiments examininginsect herbivory on eucalypts have found no relationship

between foliar terpene concentrations and the performanceof leaf-feeding beetles, the paropsine chrysomelids (Morrow& Fox, 1980; Patterson

et al

., 1996; Lawler

et al

., 1997).However, there is some suggestion from other studies thatterpenes may still have a role in conferring resistance toherbivory in eucalypts (Edwards

et al

., 1993; Stone & Bacon,1994). Similarly, high concentrations of terpenes are alsofound in the foliage of conifers, yet despite many studiesinvestigating insect herbivore–host plant interactions invarious conifer species, evidence for the importance of foliarterpenes in conferring resistance to insect herbivores isequivocal (Chen

et al

., 2002).Significant advances have been made in the past decade in

our understanding of the biochemical pathways generatingterpenoids in plants. There are two distinct biochemical



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Research2

pathways that synthesize terpenes, each partitioned into sep-arate subcellular locations: the mevalonic acid pathway, whichis located in the cytosol; and the DOXP/MEP (deoxyxylulosephosphate/methylerythritol) pathway in the plastids (Huber& Bohlmann, 2004). The former pathway produces farnesyldiphosphate, the precursor of sesquiterpenes (C15) and multiplesof these (e.g. triterpenes (C30)); the latter pathway producesthe 10-carbon intermediate geranyl diphosphate from whichmonoterpenes (C10) and diterpenes (C20) are synthesized.These precursors are then modified through dimerization andcyclization by terpene synthase enzymes (TPS) to produce adiverse array of terpene products. These products may besubsequently modified to produce other terpenoids. Theseparation of the two pathways suggests that synthesis ofthe corresponding terpene products should be regulatedindependently, although some cross-talk between the twopathways occurs ( Jux

et al

., 2001; Laule

et al

., 2003).The great diversity of terpenes found in a single plant

species are a characteristic of both the diverse array of TPSwithin the genome, and the multiple products capable ofbeing produced from a single enzyme (Huber & Bohlmann,2004). A single TPS may be capable of producing one majorcompound, or may synthesize many additional minor terpenesin characteristic ratios (Steele

et al

., 1998; Wise

et al

., 1998;Bohlmann

et al

., 1999; Faldt

et al

., 2003; Kollner

et al

., 2004).In the two best studied angiosperm systems, chemotypes of

Thymus vulgaris

and terpene profiles of different species of

Mentha

have been shown to be derived from a genetic variationvia a combination of Mendelian inheritance and epistatic inter-actions (for review see Theis & Lerdau, 2003). Quantitativetrait loci (QTL) analysis also supports major gene control ofqualitative variation in monoterpene composition in maritimepine (

Pinus pinaster

) (Plomion

et al

., 1996) and an interspecificeucalypt hybrid (

E. grandis

×

E. urophylla

) (Shepherd

et al

.,1999).

Unlike terpenes, FPCs are restricted in distribution within

Eucalyptus

to the subgenera

Symphyomyrtus

,

Eudesmia

,

Corymbia

and

Blakella

(Eschler

et al

., 2000). FPCs have been shown tobe the single most important foliar component determiningconsumption of foliage by marsupial folivores (Lawler

et al

.,2000; Wallis

et al

., 2002; Marsh

et al

., 2003; Moore

et al

.,2005). This same group of compounds has been shown toconfer resistance to Christmas beetles (

Anoplognathus

spp.)(Floyd & Foley, 2001), but variation in FPC concentrationsdoes not appear to influence canopy defoliation by paropsinechrysomelids (Henery, 2006).

Presently, very little is known about the genetic architectureand control of terpene production in the Myrtaceae, andalmost nothing is known about the biosynthesis of FPCs in

Eucalyptus

. In many species of

Eucalyptus

, such as

E. polyan-themos

(Lawler

et al

., 2000),

E. microcorys

(Moore

et al

., 2004b)and

E. melliodora

(Andrew

et al

., 2005), FPC concentrationsare strongly correlated with concentrations of the monoterpene1,8-cineole in adult leaves. The same relationship has been

detected previously in leaves of

E. nitens

seedlings (Loney

et al

., 2006); however, Close

et al

. (2003) failed to detect asimilar correlation in seedlings of the same species. It has beenshown that juvenile foliage of

E. nitens

has consistently higherterpene yields than adult foliage (Li

et al

., 1994), and thatenvironmentally derived variation in cineole concentrationdecreases (as indicated by increased estimated heritability) asyoung trees develop a mature canopy (Barton

et al

., 1991).Similarly, there is evidence that foliar FPC concentrations in

E. grandis

clonal material are not consistent over developmentaltime (I.R.W., unpublished data). This suggests that the relation-ship between monoterpenes and FPCs becomes manifest onlyon maturity of the canopy and/or ontogenetic shift from juvenileto adult foliage.

In eucalypts, both terpene and FPC foliar concentrationsare quantitative traits that exhibit large variation in foliar con-centration in a given population (Wallis

et al

., 2002; Andrew

et al

., 2005). Both terpene (Lawler

et al

., 1997; Moore

et al

.,2004b; King

et al

., 2006) and FPC (Moore

et al

., 2004b) con-centrations are known to be influenced by environmentalvariation. However, both total foliar terpene concentration(

h

2

=

0.48) (Doran & Matheson, 1994) and foliar cineoleconcentration (

h

2

=

0.83) (Barton

et al

., 1991) have beenshown to be a highly heritable traits. In addition, a study in anatural population of

E. melliodora

by Andrew

et al

. (2005)showed that foliar concentrations of both the terpene1,8-cineole and the FPC sideroxylonal were heritable andgenetically correlated.

QTL analysis has the potential to identify the number andlocation of chromosomal regions influencing the phenotypicvariation in a trait and determine the magnitude of theireffects. In this study, QTL analysis of mature progeny of acontrolled-cross family of

E. nitens

was carried out as a firststep towards elucidating the possible genetic control of terpeneand FPC accumulation in eucalyptus leaves. Specifically, wesought to identify regions of the genome influencing foliarconcentrations of FPCs and terpenes, and to determine thepercentage of variation explained by these loci. We also hopedto determine possible genetic explanations for the positivecorrelation of monoterpene and FPC foliar concentrations,given that the biochemical synthesis of these two groups ofcompounds is not likely to be closely linked (Moore

et al

.,2004a).

Materials and Methods

Plant material and field experiments

The plant material used in this QTL study came from a three-generation outcrossed pedigree constructed potentially tomaximize the variation among the progeny by choosinggrandparents originating from different parts of thegeographic distribution of

E. nitens

. The family structure isshown in Fig. 1. The first-generation trees originated from


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Research 3

open-pollinated seed collected in four different naturalpopulations from three regions of the disjunct distribution of

Eucalytpus nitens

Maiden (Figure 1 of Byrne

et al

., 1998).The second generation of the pedigree (MA

×

NN andSN

×

TO) was developed by Gunns Ltd, and the thirdgeneration was developed by CSIRO Division of Forestry andForest Products. Seedlings of the third generation wereplanted in a field experiment in north-west Tasmania, 10 kmsouth-west of Ridgley, Tasmania in 1993. Also planted in thisexperiment were individuals from three other controlled-crosspedigrees and open-pollinated seed from the same parentsof

E. nitens

. No information was available about the variationin foliar terpene concentrations between the grandparentsand parents, although previous work by Li

et al.

(1994)demonstrated variation in both terpene composition andyield between populations of

E. nitens.

The design of the field experiment was a row/column layoutconsisting of 20 rows and 11 columns, with five tree-line plotsfrom the same family in each row/column grid position. Onlyplants from the test controlled-cross pedigree were sampledfor leaf traits. Sampling was carried out in February 2005,when

c

. 50 adult, current-season, fully expanded leaves werecollected and kept refrigerated for a maximum of 7 d beforereturning them to the laboratory, where samples were imme-diately frozen.

Molecular marker data and map construction

In 1995 a genetic linkage map was constructed using the first118 progeny of the pedigree with RFLPs, allozymes and RAPDmarkers (Byrne

et al

., 1995). For this QTL study, a map wasconstructed using 296 progeny and 296 codominant markers.RFLP assay methods are detailed by Byrne

et al

. (1995);

Thamarus

et al

. (2004). Additional markers used were SSRs,genes and EST markers. The 45 SSRs that were mapped havebeen detailed previously by Thamarus

et al

. (2002); Glaubitz

et al

. (2001); Ottewell

et al

. (2005), and are prefixed on themap by a lower-case s (as in sEle006). Genotypes at 20 geneswere assayed as RFLPs (Thamarus

et al

., 2002) or SNPs(Thumma

et al

., 2005). Two sources of EST markers were alsoused. One was an

E. globulus

cambium-specific subtractedcDNA library (Bossinger & Leitch, 2000); the other a cold-stress-induced cDNA library from

E. gunnii

(C. Teulieres andC. Marques, pers. comm.). The EST markers from

E. globulus

were named as detailed in Table 1 of Thamarus

et al

. (2002).The EST markers from

E. gunnii

were labelled eUPS followedby the DNA clone name (e.g. eUPSC6a). The genetic linkagemap was constructed using the

OUTMAP

software (Butcheret al., 2002). This program provides a phase-determininglikelihood-base procedure for linkage analysis in outcrossedpedigrees.

Terpene extraction and quantification

Extraction method for terpenes and gas chromatography–mass spectroscopy (GC–MS) analysis were based on themethod of Ammon et al. (1985) except that dichloromethanerather than ethanol was used as the extraction solvent.Approximately 8 g of frozen leaf cut across the mid-section of10–15 leaves were added to a weighed volume (50 ml) ofsolvent containing tridecane as an internal standard at aconcentration of 0.25 g l–1. The extraction was then left for atleast 2 d before GC analysis. A second leaf sample of approx.1–2 g FW from the same handful of leaves as used for theterpene analysis were weighed, oven dried at 50°C for 48 h,and weighed again to determine the moisture content of eachsample. GC analysis was carried out on an Agilent (AgilentTechnologies, Inc., Santa Clara, CA, USA) 6890 N GC withhelium as the carrier gas at a flow rate of 1 ml min–1, splitinjector with split ratio of 25 : 1 (injection temperature250°C, 1 µl injection volume) and using a 60 m × 0.25 mm× 0.25 µm Heliflex (Alltech Associates, Inc., Peerfield, IL,USA) AT-35 column. Terpenes were separated using a28.5-min program with an initial temperature of 75°C heldfor 3 min, an increase of 10°C min–1 until 220°C, then 20°Cmin–1 until 275°C was reached and held for 5 min. Thecoupled mass spectrometer was an Agilent 5973 with aquadrupole mass selective detector, transfer line temperature230°C, source temperature 230°C, quadrupole temperature150°C, ionization potential 70 eV, and a scan range of 40–350 amu. Compounds were identified by comparison of massspectra with reference spectra in the NIST/EPA/NIH Massspectral library (NIST 02; NIST, Gaithersburg, MD, USA)with accompanying search program (version 2.0a). Data forquantification of terpenes were obtained from the flameionization detector coupled to the GC. Pure standards wereunavailable for developing standard curves to quantify the

Fig. 1 Structure of the pedigree used to identify quantitative trait loci influencing foliar concentrations of terpenes and formylated phloroglucinol compounds in Eucalyptus nitens. First-generation trees were from four different populations: Macalister (MA) and Toorongo (TO), central Victorian region; northern New South Wales (NN); southern New South Wales (SN).

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Research4

majority of the identified compounds, but as we were interestedin relative differences in concentrations of individual compoundsbetween progeny, not changes in ratios of concentrations ofterpenes, we calculated concentrations based on the ratio ofpeak area for each compound to the internal standard peakarea (mg internal standard equivalents per g dry leaf ). Thisavoids the complication of interdependency derived fromexpressing compounds as proportions of total terpeneyield and the difficulty in applying statistical techniques toproportions (Shepherd et al., 1999).

FPC extraction and quantification

Formylated phloroglucinol compounds were extracted accordingto the method of Wallis & Foley (2005) by sonicating 50 mgfreeze-dried ground leaf with a known mass (approx. 4.5 g) ofsolvent (7% water in acetonitrile containing 0.1% trifluoroaceticacid (TFA) and 0.3000 g l–1 of the internal standard 2-ethylphenol) for 5 min. The resulting mixture was filtered(0.2 µm) directly into an autosampler vial, then 15 µl wasinjected onto a Wakosil 250 × 4 mm GL 3C18RS (SGE)column maintained at 37°C with a flow rate of 0.75 ml min–1

on a Waters Alliance Model HPLC. The FPCs were elutedunder gradient conditions with 0.1% TFA in acetonitrile (A)and 0.1% TFA in water (B) as follows: 60% A/40% B for5 min, linear gradient to 90% A/10% B at 60 min, hold for10 min and return to starting conditions over 10 min. Wemeasured the peak response at 275 nm. The specific FPCcompounds present were quantified using authentic standardspurified in the laboratory.

Near-infrared spectroscopy

We used near-infrared spectroscopy (NIRS) to predict con-centrations of FPC compounds in foliage samples from theirnear-infrared spectra. This technique (reviewed by Foley et al.,1998) uses multivariate statistical models to relate characteristicsof the near-infrared spectra of samples of interest to attributesof a subset of samples that have been determined usingtraditional laboratory techniques. As NIR spectra are a holistic

representation of the underlying chemical composition of asample, variations in spectra can be directly related statisticallyto a component of interest (Shenk et al., 1992; Shenk &Westerhaus, 1994). The leaf samples were freeze-dried, thenground to pass a 1-mm sieve in a Tecator Cyclotec® mill (FossTecator AB, Höganäs, Sweden). We prepared the dried,ground leaf samples for scanning by placing them in a 40°Coven for at least 1 h to remove residual moisture that mightotherwise interfere with the NIR spectra. After allowing thesamples to cool to room temperature in a desiccator, weobtained the reflectance spectrum of each sample between400 and 2500 nm using an NIRSystems (Silver Spring, MD,USA) 6500 scanning spectrophotometer with spinning cupattachment. We collected duplicate spectra of each sampleuntil the root mean square of two scans (stored as log (1/reflectance)) was <3.0 × 10–4, and the two spectra wereaveraged. We developed calibration equations to predictconcentrations of constituents of interest using the referencevalues obtained from subsets of our E. nitens foliage samples bythe methods described below. We produced calibrations usingmodified partial least-squares regressions with cross-validationto prevent overfitting of models (Shenk & Westerhaus, 1991;Shenk & Westerhaus, 1994). For all calibrations, we testediteratively various mathematical transformations on the rawspectra (including first and second derivatized wavelengthsegments and smoothing), and selected models based on highr2 and low standard error of calibration. Scattering effectscaused by variation in particle size were reduced using standardnormal variate scatter correction and detrend baselinetransformations (Barnes et al., 1989). We performed all ourcalibrations using the software WINISI II ver. 1.02a (InfrasoftInternational LLC, State College, PA, USA). Our NIRSmodels are summarized in Table 1. We were able to developpredictive models for total sideroxylonal, grandinal andeucalyptin (a methyl flavanone). The isomers sideroxylonal Aand C occur in a characteristic ratio (Moore et al., 2004a), andafter quantification their respective values were summed togive a value for total sideroxylonal concentration. Grandinalis a tautomer that produces two peaks of equal size, so the areasof these were also combined. Several other compounds were

Table 1 Near-infrared spectroscopy-based modified partial least-squares regression models developed to predict foliar constituents of Eucalyptus nitens leaves

Constituent (mg g–1 leaf DW) n Mean Range

SE cross-validation r2

Wavelength range used (nm)

No. wavelengthbands used

Maths treatment*

Grandinal 33 0.38 0.17–0.97 0.094 0.96 750–2492 840 2,8,8,1Total sideroxylonal 90 2.86 1.29–8.35 0.687 0.89 750–2492 840 2,8,8,1Eucalyptin 93 0.869 0.23–1.38 0.148 0.78 408–2492 255 2,6,4,1

*Describes the mathematical treatment applied to the spectra (stored as log(1/reflectance)). The first number describes the derivative used; the second number indicates the gap size (nm) over which the derivative is calculated; the third and fourth numbers indicate the degrees of primary and secondary smoothing performed on the derivative. Thus 2,4,4,1 indicates that the second derivative was calculated with a gap size of 4 nm and that a maximal primary smooth (4) but no secondary smooth (1) was used.

© The Authors (2007). Journal compilation © New Phytologist (2007) www.newphytologist.org

Research 5

present in E. nitens in minor concentrations (macrocarpal J,euglobal III and grandinol), but we were unable to developcalibrations for these and so were excluded from QTL analyses.

QTL analyses

For each compound identified, the association between pheno-type and marker segregation was determined by using theinterval-mapping approach of QTL EXPRESS (Seaton et al.,2002), a web-based program based on the method of Haleyet al. (1994). Each linkage group was scanned at 1-cM intervalsto detect regions explaining a proportion of phenotypicvariance using the one-QTL model interval analysis. Resultsare given for QTL where F-statistics exceeded chromosome-wide significance thresholds of P < 0.05 (suggestive level) orP < 0.01 (significant) derived from 1000 permutations of thedata as described by Knott et al. (1997) and as implementedby QTL EXPRESS. Single-factor ANOVAs were also performedto test directly for the effect of marker genotype on traitphenotype. These ANOVA results were used to determine supportfor the two-QTL analysis from QTL EXPRESS as significancelevels are not currently generated by the software for thismodel. Chromosome-wide significance thresholds for thesemarker–trait associations were determined using the methodof Churchill & Doerge (1994) with separate thresholdsderived for fully informative markers (four genotypes in theprogeny) and markers segregating from either parent only(two genotypes). Confidence intervals (95%) for QTL wereestimated using the method of Darvasi & Soller (1997) whereCI = 530/nv (n = sample size; v = proportion of varianceexplained by the QTL). The linkage map was drawn usingMAPCHART (Voorrips, 2002).

Results

Terpene and FPC composition

The terpene and FPC profile of E. nitens is relatively simplecompared with other species in the subgenus Symphyomyrtus(Boland et al., 1991), and foliar concentration of terpenes hasalso previously been shown to be relatively low in comparisonwith other eucalypts (Li & Madden, 1995). Twelve terpenepeaks were identified via GC–MS that were consistentlypresent in the majority of progeny sampled. The terpeneprofile of the controlled-cross progeny used in this study iscomparable with that reported elsewhere (Li et al., 1994)for E. nitens. The monoterpene composition is dominatedby α-pinene and 1,8-cineole, and the sesquiterpenes bybicyclogermacrene and spathulenol. Strong correlations werefound between compounds within these two groups ofterpenes, and between the major monoterpenes and the FPCssideroxylonal and grandinal (Table 2). The distributions offoliar concentrations in the progeny did not differ greatlyfrom normality. Ta

ble

2C

orre

latio

ns b

etw

een

folia

r con

cent

ratio

ns o

f mon

oter

pene

s (c

ompo

unds

1–8

), s

esqu

iterp

enes

(com

poun

ds 9

–12)

, for

myl

ated

phl

orog

luci

nol c

ompo

unds

(com

poun

ds 1

3 an

d 15

) and

eu

caly

ptin

(14

) in

Euc

alyp

tus

nite

ns p

roge

ny (

head

ings

are

abb

revi

ated

ver

sion

s of

com

poun

ds li

sted

in t

he fi

rst

colu

mn)

Trai

tPi

neph

elcy

me

limo

cine

tpin

pino

terp

allo

bicy

spat

viri

sid

euc

gra

α-pi

nene

1.00

0α-

phel

land

rene

0.46

61.

000

p-cy

men

e–

0.17

00.

108

1.00

0lim

onen

e0.

891

0.54

20.

008

1.00

01,

8-ci

neol

e0.

805

0.36

7–

0.12

60.

809

1.00

0tr

ans-

pino

carv

eol

–0.

261

–0.2

91–

0.03

6–

0.49

8–

0.00

71.

000

pino

carv

one

–0.

311

–0.3

54–

0.07

3–

0.54

8–

0.06

70.

947

1.00

0α-

terp

ineo

l0.

725

0.36

0–

0.11

00.

779

0.74

2–

0.29

2–

0.33

51.

000

allo

arom

aden

dren

e–

0.02

70.

360

0.82

40.

124

–0.

029

–0.

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–0.

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–0.

031

1.00

0bi

cycl

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0.29

40.

835

0.25

30.

421

0.17

8–

0.34

7–

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90.

168

0.58

51.

000

spat

hule

nol

–0.

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–0.0

340.

896

–0.

120

–0.

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–0.

010

–0.

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–0.

206

0.84

90.

181

1.00

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ridifl

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0.16

60.

189

0.31

20.

215

0.26

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0.04

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158

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50.

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0.27

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31.

000


Research6

QTL analyses

A total of 45 significant loci were detected for 14 traits(terpene and FPC foliar concentrations) using one-QTLmodels in QTL EXPRESS (Table 3). These QTL were distributedacross multiple linkage groups (Fig. 2a,b). However, genomicpositions of these QTL demonstrated significant clusteringon linkage groups 7, 8 and 9. QTL for chemically relatedgroups of compounds often mapped to both the same linkagegroups and approximately the same locations on these linkagegroups (Fig. 2a,b). Most notably, on linkage group 8, QTLwere identified for the monoterpene compounds α-pinene,1,8-cineole, limonene and α-terpineol between adjacentmarkers at 143 and 172 cM (Fig. 2b). The QTL on linkagegroup 8 were all highly significant, and the percentage ofvariance explained by these loci ranged between 11.6 and 16.9%.This concordance of results is probably caused by the closephenotypic correlations between the foliar concentrations ofthese compounds. QTL for these traits also mapped toidentical locations on linkage groups 7 and 9, the exceptionsbeing 1,8-cineole on chromosome 9, for which the varianceratio was not significant at this location, and α-terpineol onlinkage group 7, which appears to be significantly associatedwith two regions on this linkage group (support from atwo-QTL model in Table 4). These QTL, significant at the

suggestive level, accounted for small proportions of variationthat were similar in magnitude for each trait. Accordingly,these colocating QTL could be considered a single QTL oneach linkage group influencing the concentration of theserelated compounds. The best estimates for locations of QTLfor the two FPCs sideroxylonal and grandinal were close tothose for monoterpenes on linkage groups 7, 8 and 9.

The estimates for the individual parental effect of theseQTL on trait values (difference in the effect of the two QTLalleles inherited from each parent) indicate that inheritance ofan allele from the maternal parent has consistent positiveeffects (‘maternal effect’ in Table 3) for both the aforementionedmonoterpenes and FPCs (traits 1, 4, 5, 8, 13 and 15 inTable 2). Examination of mean values for progeny genotypes,and segregation of the flanking fully informative markers atthese locations on linkage group 8, suggest that increases infoliar concentrations of monoterpenes and the FPCs sidero-xylonal and grandinal are caused by alleles inherited from thefemale grandparent on the maternal side (MA) (Table 4). Forexample, higher than average cineole concentrations in theprogeny are associated with inheritance of the marker alleledenoted as 2 at the marker g042B, which originates in the MAgrandparent (Table 4). The fully informative markers flank-ing the QTL on linkage group 9 show a different pattern ofinheritance with segregation of marker alleles from the female

Fig. 2 Map locations of quantitative trait loci on linkage groups (a) 1–4; (b) 7–11 for foliar concentrations of formylated phloroglucinol compounds and eucalyptin (squares), monoterpenes (circles) and sesquiterpenes (triangles) in Eucalyptus nitens. Bars are 95% CI. Traits with two QTL per linkage group (LG) are indicated by numbering. Trait codes are defined in Table 3. Scale bar indicates distance in cM.


Research 7

grandparent on the paternal side (SN) associated with higherconcentrations of monoterpenes and FPCs. There is also aninteraction with alleles segregating from the MA grandparent,which reduces the positive influence of the allele from the SNgrandparent on foliar concentrations of these compounds(Table 4). The origin of an allele positively influencing mono-

terpene and FPC concentrations on linkage group 7 is moredifficult to determine, as there appears to be a positive inter-action between two alleles at this QTL (Table 4).

For the monoterpenes trans-pinocarveol and pinocarvone,highly significant QTL were detected at the same locationbetween neighbouring fully informative markers (positions

Fig. 2 continued.


Research8

93 and 105.7 cM) on linkage group 7 (Table 3; Fig. 2b).Similarly, QTL for these compounds locating within 6 and1 cM of each other were identified between neighbouringmarkers on linkage groups 3 and 11, respectively. The veryhigh positive correlation between these biosynthetically relatedcompounds suggests these are also single QTL influencing

the concentration of both compounds. Analysis of markersegregation at these two loci shows that higher than averageconcentrations of these compounds are associated with inher-itance of alleles from different grandparents at each QTL.Interestingly, the QTL for trans-pinocarveol that was detectedon linkage group 3 showed a far greater level of significance at

Table 3 Quantitative trait loci (QTL) identified from analysis of concentrations of foliar chemical data using a one-QTL model

LG Trait* Significance†Position (cM) n

Mean (SE) (mg per g leaf DW)

Paternal effect (SE)

Maternal effect (SE)

Percentagevariation‡

1 sid a 174 281 2.818 (0.07) 0.189 (0.076) –0.111 (0.078) 5.4euc b 180 281 0.885 (0.011) –0.031 (0.012) –0.016 (0.011) 3.9

2 cine b 101 269 1.322 (0.031) 0.018 (0.033) 0.061 (0.034) 4.5bicy a 155 269 0.118 (0.007) 0.010 (0.007) –0.027 (0.007) 6.8

3 cyme b 171 269 0.181 (0.004) 0.016 (0.006) 0.009 (0.006) 4.2cine b 36 269 1.329 (0.031) 0.076 (0.036) 0.111 (0.031) 5.1tpin a 24 269 0.065 (0.002) 0.016 (0.003) 0.004 (0.003) 11.9pino b 30 269 0.05 (0.002) 0.008 (0.002) 0.003 (0.002) 3.8allo b 171 269 0.036 (0.001) 0.004 (0.001) 0.003 (0.001) 8.3bicy b 21 269 0.133 (0.005) 0.023 (0.007) 0.002 (0.007) 4.9spat b 171 269 0.262 (0.006) 0.018 (0.009) 0.024 (0.009) 4.7

4 tpin b 113 269 0.064 (0.002) –0.002 (0.002) 0.007 (0.002) 4.9pino b 113 269 0.05 (0.002) –0.002 (0.002) 0.006 (0.002) 4.9

7 pine a 112 269 0.774 (0.026) –0.135 (0.027) 0.079 (0.028) 11.5cyme b 90 269 0.181 (0.004) 0.016 (0.005) 0.005 (0.005) 4.1limo b 112 269 0.083 (0.003) –0.008 (0.003) 0.008 (0.003) 4.6cine b 116 269 1.318 (0.031) –0.098 (0.033) –0.009 (0.033) 4.3tpin a 93 269 0.065 (0.002) –0.010 (0.002) –0.004 (0.002) 6.0pino a 93 269 0.051 (0.002) –0.009 (0.002) –0.003 (0.002) 6.1terp a 60 269 0.036 (0.002) –0.003 (0.002) 0.009 (0.002) 5.7viri b 22 269 0.033 (0.001) 0.003 (0.002) 0.004 (0.001) 3.7sid a 109 281 2.822 (0.07) –0.168 (0.073) 0.184 (0.074) 4.8

8 pine a 153 269 0.784 (0.025) –0.238 (0.032) 0.048 (0.028) 16.9cyme b 184 269 0.183 (0.004) 0.018 (0.005) –0.007 (0.004) 5.2limo a 165 269 0.084 (0.003) –0.022 (0.004) 0.000 (0.003) 12.4cine a 154 269 1.325 (0.029) –0.274 (0.037) 0.059 (0.032) 16.8terp a 153 269 0.035 (0.002) –0.015 (0.002) 0.003 (0.002) 11.6allo b 187 269 0.036 (0.001) 0.003 (0.001) –0.002 (0.001) 6.3spat a 182 269 0.265 (0.006) 0.032 (0.007) –0.015 (0.006) 8.3sid a 149 281 2.832 (0.068) –0.534 (0.084) 0.175 (0.072) 12.6euc b 137 281 0.884 (0.011) 0.057 (0.014) –0.025 (0.015) 5.0gra a 138 281 0.353 (0.01) –0.055 (0.013) 0.030 (0.013) 6.7

9 pine b 97 269 0.779 (0.027) 0.036 (0.027) 0.105 (0.028) 4.9cyme b 76 269 0.354 (0.01) –0.011 (0.004) 0.019 (0.004) 8.3limo b 96 269 0.084 (0.003) 0.001 (0.003) 0.012 (0.003) 3.9terp b 95 269 0.035 (0.002) 0.001 (0.002) 0.008 (0.002) 3.8allo b 106 269 0.036 (0.001) –0.002 (0.001) 0.003 (0.001) 6.7spat a 74 269 0.264 (0.006) –0.018 (0.007) 0.024 (0.007) 5.9viri a 91 269 0.032 (0.001) –0.003 (0.001) 0.005 (0.001) 5.5sid a 98 281 2.829 (0.068) 0.178 (0.071) 0.336 (0.072) 8.1gra a 102 281 0.356 (0.01) 0.035 (0.010) 0.031 (0.010) 6.1

10 limo b 33 269 0.083 (0.003) –0.003 (0.004) –0.012 (0.004) 3.4sid b 30 281 2.815 (0.07) –0.021 (0.081) –0.201 (0.083) 4.1

11 tpin a 13 269 0.063 (0.002) 0.012 (0.003) –0.001 (0.004) 7.8pino a 12 269 0.05 (0.002) 0.010 (0.002) –0.002 (0.003) 7.7

*Traits: pine, α-pinene; cyme, p-cymene; limo, limonene; cine, 1,8-cineole; tpin, trans-pinocarveol; pino, pinocarvone; terp, α-terpineol; allo, alloaromadendrene; bicy, bicyclogermacrene; spat, spathulenol; viri, viridiflorol; sid, total sideroxylonal; euc, eucalyptin; gra, grandinal.†Significance level based on 1000 chromosome wide permutations: a, P < 0.01; b, 0.01 ≤ P < 0.05.‡Percentage variance explained by QTL.


Research 9

this location (P < 0.01) and explained a far greater proportionof variance (11.6 vs 3.8%) than the QTL for pinocarvone.

The QTL for sesquiterpenes (traits 9, 10, 11, 12) werelocated on the same linkage groups as those for other terpenesand FPCs, but were also detected on linkage groups 2 and 3.Where QTL were identified on the same linkage groups asmonoterpenes (linkage groups 3, 7 and 8), best estimates oflocations from one-QTL models generally identified differentlocations as important (the exception is the monoterpenep-cymene; see Discussion for explanation). In addition, inves-tigations of flanking informative markers commonly indicatethat inheritance of alleles for increased concentrations of thesecompounds are derived from different parents and/or grand-parents to those of monoterpenes. For example, the QTL forspathulenol on linkage group 8 is located near the QTL forthe monoterpenes discussed previously (approx. 30 cM apart)(Fig. 2b). Segregation of flanking markers indicates that alle-les associated with a higher than average foliar spathulenolconcentration are inherited from the male grandparent on thematernal side (NN) and not the female grandparent (MA), aswas the case with the monoterpenes (Table 4). There alsoappears to be an interaction with an allele inherited from themale parent that reduces the positive effect of this allele.

Results for fitting two QTL models on linkage groupswhere high levels of significance were identified from theinitial analyses generally did not support the presence of twoQTL in most instances. Additional QTL did not explain asignificant proportion of variance beyond that accounted forby a one-QTL model. This finding is generally supported bycomparing the best estimates of the locations of QTL fromthe one- and two-QTL models with loci identified as signifi-cant by individual ANOVAs. Markers identified as significantusing threshold F-values derived from permutation testinggenerally corresponded to locations flanking best estimatelocations from one-QTL models. There were two QTL (sig-nificant at the suggestive threshold) that were exceptions tothis (Table 5). In both these cases, fitting two QTL improvedthe variance accounted for, and the proposed locations matchedlocations of markers that were associated with relatively highF-values in single ANOVAs (although not greater than thresholdvalues based on permutations).

Although two-QTL models did not support the presenceof more than one QTL for any traits on linkage group 8, thereis some evidence to suggest that multiple loci that affect traitsare present in this genomic region (Fig. 3). Using 1,8-cineoleas an example, single ANOVAs identified highly significantmarker–trait associations in five different locations between90 and 190 cM along this linkage group. Marker segregationat all these loci suggests that inheritance of alleles from thefemale parent is associated with a positive effect on foliarconcentration of 1,8-cineole. These markers are separated byintervals of between 20 and 50 cM, and two QTL with thesame direction of effect are unlikely to be detected by intervalmapping when located this close together (Knott et al., 1997).Ta

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Research10

In the case of the outer markers (100 cM apart), detection oftwo QTL may also be prevented because a QTL of greatereffect operating in the same direction exists between theselocations (at 154 cM as estimated by the one-QTL model).

Separating QTL in this particular region on linkage group 8is also made difficult because of the low information contentof intervening markers between regions identified as signifi-cant. However, the genomic distance between significantmarker trait associations on this linkage group suggests thatmultiple QTL may be present.

Discussion

This study has identified multiple regions of the E. nitensgenome that influence the concentrations of individualfoliar terpenes and FPCs. While many of the QTL detectedfor different traits do not appear to be unique, for manycompounds or groups of related compounds we were able todetermine that loci located on different linkage groups appearto influence these traits. The ability to detect multiple loci wasenhanced by the relatively large number of progeny sampled,the minimization of environmental heterogeneity by use of asingle, relatively homogeneous site, the sampling of evenlyaged leaves from mature foliage of established trees, and thegenetic structure of the population. The alleles segregatingin this outbred pedigree originate from four differentpopulations of E. nitens, a species with a highly disjunctdistribution (Cook & Ladiges, 1991). Studies of nuclear andchloroplast genetic diversity in E. nitens demonstrate thatdiversity is high in this species and the majority of the nuclearvariation is distributed within populations, but there issignificant variation between populations and also regions(Byrne & Moran, 1994; Byrne et al., 1998). Therefore it islikely that allelic variation in genes affecting foliar chemistrywould be detected, given that there is known to be variationbetween populations in both total terpene yield as well as theproportional composition of terpenes in E. nitens (Li et al.,1994). Similarly, FPC concentrations are known to varybetween populations in other eucalypt species (O’ReillyWapstra et al., 2005). Consequently, it is also not unexpectedto find alleles originating from different populations affectingcompounds with different biosynthetic origins or evenexerting effects on the same trait at different QTL.

QTL for wood quality traits have been identified in com-mercially important forest trees (Grattapaglia et al., 1996;Verhaegen et al., 1997; Sewell et al., 2000; Brown et al., 2003;Thamarus et al., 2004). Despite high heritabilities for wood

Table 5 Results from analysis using two-quantitative trait loci (QTL) models for traits found to be significant by permutation testing in one-QTL models and where two QTL positions were supported by single ANOVAs

Linkagegroup Trait

Position (cM)

Mean (SE) (mg g–1 leaf DW)

Paternal effect (SE)

Maternal effect (SE)

Percentagevariation

7 α-terpineol 58 0.035 (0.002) 0.002 (0.002) –0.008 (0.002) 9.1110 0.006 (0.002) –0.001 (0.002)

3 spathulenol 76 0.260 (0.006) 0.014 (0.007) –0.009 (0.007) 7.6170 –0.020 (0.009) 0.013 (0.010)

Fig. 3 Map showing marker loci on linkage group (LG) 8 found to be significant for genotype–trait associations at P < 0.05 level by single-factor ANOVA with permutation tests. Traits significant at individual marker loci are indicated by arrows, with trait codes as defined in Table 3. Sideroxylonal is shown in bold; monoterpenes in plain text; sesquiterpenes in italics. *, Markers that are fully informative.


Research 11

and pulp quality traits (h2 = 0.4–0.8) (Whiteman et al., 1996),QTL studies have commonly found the genetic architectureof wood traits to be polygenic in nature, with multiple QTLof small effect identified (Thamarus et al., 2004). These find-ings reflect the complexity of wood traits. The properties ofwood tissue are dependent on many chemical componentsincluding lignin, cellulose and hemicelluose produced byindependent biosynthetic pathways. In contrast to woodtraits, while heritabilities for foliar secondary metabololitesin eucalypts are similarly high (h2 = 0.83 for cineole, Bartonet al., 1991; 0.6 for sideroxylonal, R. L. Andrew et al., unpub-lished data), we found that the foliar chemistry of eucalyptsmay be affected by a few loci with relatively large effects onterpene or FPC foliar concentrations. Terpene and FPC con-centrations are likely to directly reflect genetic differences thatcontrol enzymatic steps in the biosynthetic pathway producingeach compound. By comparison, a composite trait such asproportion of total terpene content is confounded by altera-tions to the concentration of individual components forwhich synthesis may be differentially regulated (Shepherdet al., 1999).

We detected several genomic locations where QTL forseveral traits colocated. For many of these QTL, notably themonoterpenes α-pinene, 1,8-cineole, limonene and α-terpineol(on linkage groups 7, 8 and 9), traits exhibited strong pheno-typic correlations and common inheritance of flanking markeralleles from grandparents (linkage group 8). A single QTLexerting pleiotropic effects on the concentration of relatedmonoterpenes is expected, given their common biosyntheticorigins. As has been demonstrated in expression studies(Martin et al., 2004; Shimada et al., 2004), monoterpenesynthases convert the precursor molecule geranyl diphosphateinto the majority of monoterpenes found in leaves (e.g. α-pinene, and 1,8-cineole). Thus for the aforementioned groupof monoterpenes, foliar concentrations may be strongly influ-enced by either alteration of the activity of the TPS producingthem, or changes to the activity of enzymes in the pathwayproducing the substrate geranyl diphosphate.

An intriguing result was the mapping of a QTL for sider-oxylonal (but also in some locations grandinal and eucalyptin),either very close to or in identical locations to QTL influenc-ing monoterpene concentration, because this compound issuspected to be biosynthetically unrelated. The chemicalpathway leading to the formation of a group of compoundsthat includes acyl-phloroglucinols, FPCs and β-triketones, asubset of the tetraketides, are currently unknown. However,their structures correspond to known products of chalconesynthases, which catalyse polyketide chain extensions followedby formation of the phloroglucinol core structure (Keszei, 2006).The proposed biosynthetic scheme most likely to describetheir formation suggests no commonality of precursors withterpenes, particularly monoterpenes, with which they areclosely correlated in eucalypts (Keszei, 2006). This suggeststhat the QTL for these groups are either very closely linked,

or represent the location of a common regulatory regiongoverning transcription of genes from both pathways.

Trans-pinocarveol and pinocarvone are probably synthe-sized from α-pinene in two separate enzymatic modifications(Keszei, 2006). Colocation of QTL at two genomic regionsfor these compounds, and their close phenotypic correlation,suggest that changes in the concentrations of either trans-pinocarveol or pinocarvone are mirrored by correspondingincreases or decreases in the other compound, and that enzy-matic activity for this second conversion step is not limiting.Thus these two QTL probably influence the first enzymaticconversion in this process and consequently the rate of sub-strate supply for the formation of both these compounds.However, identification of a highly significant QTL for trans-pinocarveol in another location where the QTL for pinocarvoneis barely significant suggests that this third QTL influences theenzyme converting trans-pinocarveol to pinocarvone as thesecond step in the process. Using as a guide the biochemicalsteps characterized from monoterpene synthesis in mint(Lange & Croteau, 1999), the proposed reaction mechanismwould be an oxidation of α-pinene, putatively catalysed by acytochrome P450 oxygenase to form trans-pinocarveol, followedby reduction to form pinocarvone.

In contrast to the monoterpenes, QTL influencing foliarconcentrations of the various sesquiterpenes (traits 9–12) didnot tend to colocate with each other or with monoterpenes orFPCs. In addition, even where the direction of effect was thesame as for closely located QTL for monoterpenes, analysis ofinheritance showed that alleles positively affecting the twotraits were derived from different grandparents (e.g. spathulenolon linkage group 8). This is a reflection of the two separatebiosynthetic pathways that produce these two groups ofterpenes. Two exceptions are the identification of two QTL atthe same location for alloaromadendrene and spathulenol onlinkage group 3, and the close association of QTL for spathule-nol and the monoterpene p-cymene. No TPS isolated thus faris capable of producing p-cymene in expression assays (Keszei,2006). However, it can be produced by spontaneous oxidationof monoterpenes via processes such as leaf ageing or duringsome extraction methods (Sefidkon et al., 1999; Zabaras &Wyllie, 2002). Spathulenol is probably produced by a reactionmechanism involving enzymatic oxidation of bicyclogerma-crene (Keszei, 2006). The generation of both p-cymene andspathulenol by oxidative processes is a mechanistic link thatmay explain the phenotypic correlation and common loca-tions of QTLs for these otherwise unrelated terpenes. Theidentification of separate genomic regions influencingindividual sesquiterpene concentrations is probably caused bymultiple downstream enzymatic modifications after cyclizationby terpene synthases required to produce these compounds.

As with any QTL study, we acknowledge that there arelimitations to these findings. QTL will be detected in a set ofprogeny only if alleles with different effects on a trait are seg-regating at that location in the pedigree. Similarly, genetically


Research12

correlated traits may not map to common QTL locations ifloci for regulatory genes controlling both traits lack meaning-ful polymorphisms (Remington & Purugganan, 2003). Thisstudy examined only one cross, and therefore examination ofother pedigrees where different segregating alleles are presentmay reveal additional QTL. In addition, while our sample sizeis reasonable, simulation studies indicate that when progenysample sizes are <500, QTL of small effect are unlikely to bedetected, and the effect of those QTL that are detected will beoverestimated (Beavis, 1998). Thus the effect size of some ofthe QTL presented here should be interpreted with a degreeof caution.

It is well recognized that in highly heterozygous, outbredorganisms such as trees, where gene × environment inter-actions may be important in large plantation experiments, itis important to verify QTL results in another set of progenyand/or in another pedigree at other sites (Kumar et al., 2000;Brown et al., 2003; Thamarus et al., 2004). However, as hasbeen argued elsewhere, even after verification of QTL theapplication of marker-based approaches for selection of desir-able traits in tree breeding is limited (Strauss et al., 1992;Brown et al., 2003; Thamarus et al., 2004; Wheeler et al., 2005).In addition, sampling leaves from mature tree canopies forsignificant numbers of progeny and subsequently quantifyingfoliar chemistry is both difficult and expensive. The low reso-lution of linkage maps in trees suggests that, in the case oflinkage group 8, where QTL for a large number of differentcompounds are located within a small region with relativelyfew fully informative markers, significant advances in dissect-ing genetic architecture via a repeated mapping approach isunlikely. Therefore, as discussed by Wheeler et al. (2005), theapproach most likely to achieve further understanding of thegenetic basis of foliar secondary metabolite variation in euca-lypts would be identification and mapping of candidate genesto QTL locations identified here. With a more completeunderstanding of terpene and FPC synthesis, we may be ableto determine which QTL are associated with flux-regulatingpoints on their respective pathways. Such an approach hasbeen used in a study of flavonoid biosynthesis in Populus(Morreel et al., 2006). Genes that map to the appropriatelocation and other candidate genes could also be tested forassociations between gene sequence variation and the chemicalphenotypes. This approach has been used successfully for woodtraits in trees (Thumma et al., 2005; Gonzalez-Martinezet al., 2007).

A study utilizing genome sequence analysis to identify andlocate TPS in the Arabidopsis thaliana genome has shown thatTPS often occur in close proximity as tandem gene duplicates,and are frequently closely associated with other genes poten-tially involved in terpene synthesis (Aubourg et al., 2002). Inthe same species, but for another group of secondary meta-bolites, a gene duplication within a small gene family has beenshown to generate two different enzymes that control variationin glucosinolate profiles (Kliebenstein et al., 2001). The infer-

ence that can be made from these studies, and others, is thatsecondary metabolite diversity is likely to have arisen by geneduplications, and consequently genes having significanteffects on variation in secondary metabolites may be locatedvery close together in the genome (Ober, 2005). Thus, whilethe genomic region of interest may be large, genomicsequencing to locate and determine the structure of any genefamilies in the vicinity of QTL locations may prove valuablefor future identification of genotype–phenotype associationsfor foliar secondary metabolites.

Acknowledgements

We acknowledge the work of Charlie Bell and Jan Murrell ofEnsis/CSIRO for molecular marker assays and map con-struction in E. nitens. The assistance with field collections byJohn Owen and Leroy Stewart of Ensis/CSIRO is gratefullyacknowledged. Frances Marsh assisted with field collectionsand NIR analyses. We thank Gunns Pty Ltd for access to thetrial. M.L.H. was financially supported by the AustralianResearch Council and Forests NSW.

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Identiﬁcation of quantitative trait loci inﬂuencing foliar ... · Martin L. Henery, Gavin F. Moran, Ian R. Wallis and William J. Foley School of Botany and Zoology, Australian